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Originally published as Genetics Published Articles Ahead of Print on April 19, 2006.
Genetics, Vol. 173, 1693-1703, July 2006, Copyright © 2006
doi:10.1534/genetics.105.048108
On Locating Multiple Interacting Quantitative Trait Loci in Intercross Designs
Andreas Baierl*,1,
Ma
gorzata Bogdan
,
Florian Frommlet* and
Andreas Futschik*
* Institute of Statistics and Decision Support Systems, University of Vienna, A-1010 Vienna, Austria and Institute of Mathematics and Computer Science, Wrocaw University of Technology, 50-370 Wrocaw, Poland
1 Corresponding author: Institute of Statistics and Decision Support Systems, University of Vienna, Universitaetsstrasse 5, A-1010 Vienna, Austria.
E-mail: andreas.baierl{at}univie.ac.at
A modified version (mBIC) of the Bayesian Information Criterion (BIC) has been previously proposed for backcross designs to locate multiple interacting quantitative trait loci. In this article, we extend the method to intercross designs. We also propose two modifications of the mBIC. First we investigate a two-stage procedure in the spirit of empirical Bayes methods involving an adaptive (i.e., data-based) choice of the penalty. The purpose of the second modification is to increase the power of detecting epistasis effects at loci where main effects have already been detected. We investigate the proposed methods by computer simulations under a wide range of realistic genetic models, with nonequidistant marker spacings and missing data. In the case of large intermarker distances we use imputations according to Haley and Knott regression to reduce the distance between searched positions to not more than 10 cM. Haley and Knott regression is also used to handle missing data. The simulation study as well as real data analyses demonstrates good properties of the proposed method of QTL detection.
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